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14 April 2008 Analysis of an autonomous clutter background characterization method for hyperspectral imagery
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Hyperspectral ground to ground viewing perspective presents major challenges for autonomous window based detection. One of these challenges has to do with object scales uncertainty that occur when using a window-based detection approach. In a previous paper, we introduced a fully autonomous parallel approach to address the scale uncertainty problem. The proposed approach featured a compact test statistic for anomaly detection, which is based on a principle of indirect comparison; a random sampling stage, which does not require secondary information (range or size) about the targets; a parallel process to mitigate the inclusion by chance of target samples into clutter background classes during random sampling; and a fusion of results at the end. In this paper, we demonstrate the effectiveness and robustness of this approach on different scenarios using hyperspectral imagery, where for most of these scenarios, the parameter settings were fixed. We also investigated the performance of this suite over different times of the day, where the spectral signatures of materials varied with relation to diurnal changes during the course of the day. Both visible to near infrared and longwave imagery are used in this study.
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João M. Romano, Dalton Rosario, Luz Roth, Eric Roese, and Paul Willson "Analysis of an autonomous clutter background characterization method for hyperspectral imagery", Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69661Q (14 April 2008);

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